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Performance

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THIS IS A MOCKUP VERSION PLEASE DO NOT CITE

The Performance section measures participants’ practical digital skills across a range of real-life tasks. Instead of self-reports, these items test what people can actually do: for example, searching for reliable information, recognizing AI-generated images, protecting their devices, or using AI tools effectively. The items cover ten areas of digital competence, from strategic and critical information skills to AI and generative AI skills, providing an overall picture of how well individuals can navigate today’s digital environment.

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Digital Content Creation: Performance Questions

Information literacy scores were highest at 85%. Participants excelled at search strategies and source evaluation.

Strategic Information

Strategic Information Skills assess the ability to effectively search for and locate information online. This includes choosing good keywords, using search functions, and finding answers to questions on the internet.

Performance tasks: proportion correct or selected. Where items are multi-select, we show the share selecting each action.

See all Strategic Information results

  • Wave 1
  • Wave 2
  • Over Time
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Question 1

Critical Information

Critical Information Skills measure the ability to evaluate online information: checking whether information is true, assessing website reliability, and understanding the purpose of online content (to inform, influence, entertain, or sell).

Look closely at this post on social media. What kind of post do you think this is?

See all Critical Information results

  • Wave 1
  • Wave 2
  • Over Time
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3
  • Question 1
  • Question 2
  • Question 3

Digital Content Creation

Digital Content Creation skills cover the ability to create and modify digital content: making presentations, combining different media, editing images/music/video, and understanding copyright rules around digital content.

Which of the following icons refer to the function for cutting or removing parts of a picture ("cropping")?

See all Digital Content Creation results

  • Wave 1
  • Wave 2
  • Over Time
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Question 1

Netiquette

Netiquette refers to proper online communication etiquette: knowing when to ask permission before sharing, choosing the right communication tool, understanding what not to share online, and using emoticons appropriately.

Performance tasks: proportion correct or selected. Where items are multi-select, we show the share selecting each action.

See all Netiquette results

  • Wave 1
  • Wave 2
  • Over Time
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
  • Question 1
  • Question 1
  • Overall
  • Age
  • Gender
  • Education
  • Question 1
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Source Code
---
title: "{{< iconify ph clipboard-text >}} Performance"
format: html
---


```{=html}
<script>document.body.classList.add('has-pagination-top');</script>
<nav class='pagination-nav pagination-top' role='navigation' aria-label='Page navigation'>
  <div class='pagination-container'>
    <button class='pagination-btn pagination-prev pagination-disabled' disabled aria-label='Previous page'>
      <svg class='pagination-icon' width='18' height='18' viewBox='0 0 20 20' fill='none' xmlns='http://www.w3.org/2000/svg'>
        <path d='M12 16L6 10L12 4' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/>
      </svg>
    </button>
    <div class='pagination-info'>
      <span class='pagination-prefix'>Page </span>
      <input type='number' class='pagination-input' id='page-input-top' min='1' max='3' value='1' aria-label='Current page'>
      <span class='pagination-separator'> / 3</span>
    </div>
    <a href='performance_p2.html' class='pagination-btn pagination-next' aria-label='Next page'>
      <svg class='pagination-icon' width='18' height='18' viewBox='0 0 20 20' fill='none' xmlns='http://www.w3.org/2000/svg'>
        <path d='M8 16L14 10L8 4' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/>
      </svg>
    </a>
  </div>
</nav>

<!-- Pagination Navigation Script -->
<script>
(function() {
  const pageUrls = ["performance.html","performance_p2.html","performance_p3.html"];
  const pageInput = document.getElementById('page-input-top');
  
  if (pageInput) {
    pageInput.addEventListener('change', function() {
      const pageNum = parseInt(this.value);
      if (pageNum >= 1 && pageNum <= pageUrls.length) {
        window.location.href = pageUrls[pageNum - 1];
      } else {
        this.value = this.getAttribute('value');
      }
    });
    
    pageInput.addEventListener('keypress', function(e) {
      if (e.key === 'Enter') {
        this.blur();
      }
    });
  }
})();
</script>
```



```{r}
#| include: false
library(dashboardr)
```

**THIS IS A MOCKUP VERSION PLEASE DO NOT CITE**

The **Performance** section measures participants' practical digital skills across a range of real-life tasks. Instead of self-reports, these items test what people *can actually do*: for example, searching for reliable information, recognizing AI-generated images, protecting their devices, or using AI tools effectively. The items cover ten areas of digital competence, from strategic and critical information skills to AI and generative AI skills, providing an overall picture of how well individuals can navigate today's digital environment.

```{r, echo=FALSE}
dashboardr::enable_modals()
```

```{r setup}
#| echo: false
#| warning: false
#| message: false
#| error: false
#| results: 'hide'

# Load required libraries
library(dashboardr)
library(dplyr)
library(highcharter)

# Global chunk options
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  error = FALSE,
  fig.width = 12,
  fig.height = 8,
  dpi = 300
)

# Load data from dataset_4014obs.rds
data <- readRDS('dataset_4014obs.rds')

# Data summary
cat('Dataset loaded:', nrow(data), 'rows,', ncol(data), 'columns\n')

# Create filtered datasets
# Each filter is applied once and reused across visualizations

data_filtered_984a0efe <- data %>% dplyr::filter(wave == 1)
data_filtered_4af682fd <- data %>% dplyr::filter(wave == 2)

```


```{r, echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
# Use dashboardr's loading overlay function
dashboardr::add_loading_overlay("Loading", 1, theme = "light")
```


```{r, echo=FALSE, results='asis'}
cat(as.character(dashboardr::modal_content(
  modal_id = 'PDCCS1R',
  text = '<h2>Digital Content Creation: Performance Questions</h2>
<img src="https://placehold.co/600x400/EEE/31343C" style="max-width:100%; height:auto;">
<p>Information literacy scores were highest at 85%.
                     Participants excelled at search strategies and
                     source evaluation.</p>'
)))
```

## {{< iconify ph magnifying-glass >}} Strategic Information


**Strategic Information Skills** assess the ability to effectively search for and locate information online. This includes choosing good keywords, using search functions, and finding answers to questions on the internet.

```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote("Performance tasks: proportion correct or selected. Where items are multi-select, we show the share selecting each action.", preset = "question")
```
[{{< iconify ph cards >}} See all Strategic Information results](strategic_information.html)


::: {.panel-tabset}

### {{< iconify ph number-circle-one-fill >}} Wave 1


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-sis-wave1-overall}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbars(
  data = data_filtered_984a0efe %>% tidyr::drop_na(PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  questions = "PSIS2R",
  question_labels = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-wave1-age-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PSIS2R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-wave1-gender-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PSIS2R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-wave1-edu-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PSIS2R"
)

result
```


:::


:::


### {{< iconify ph number-circle-two-fill >}} Wave 2


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-sis-wave2-overall}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbars(
  data = data_filtered_4af682fd %>% tidyr::drop_na(PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  questions = "PSIS2R",
  question_labels = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-wave2-age-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PSIS2R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-wave2-gender-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PSIS2R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-wave2-edu-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PSIS2R),
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PSIS2R"
)

result
```


:::


:::


### {{< iconify ph chart-line-fill >}} Over Time


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-overtime-overall-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_timeline(
  data = data,
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  response_filter_label = "Percentage who selected/answered correctly",
  response_filter_combine = TRUE,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  response_var = "PSIS2R"
)

result
```


:::


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-overtime-age-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_timeline(
  data = data,
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PSIS2R",
  group_var = "AgeGroup"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-overtime-gender-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_timeline(
  data = data,
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PSIS2R",
  group_var = "geslacht"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-sis-overtime-edu-item1}
# Restrict Google to Dutch sources (correct/incorrect)
result <- create_timeline(
  data = data,
  title = "Restrict Google to Dutch sources (correct/incorrect)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PSIS2R",
  group_var = "Education"
)

result
```


:::


:::


:::

## {{< iconify ph detective-fill >}} Critical Information


**Critical Information Skills** measure the ability to evaluate online information: checking whether information is true, assessing website reliability, and understanding the purpose of online content (to inform, influence, entertain, or sell).

```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote("Look closely at this post on social media. What kind of post do you think this is?", preset = "question")
```
[{{< iconify ph cards >}} See all Critical Information results](critical_information.html)


::: {.panel-tabset}

### {{< iconify ph number-circle-one-fill >}} Wave 1


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-cis-wave1-overall}
# 
result <- create_stackedbars(
  data = data_filtered_984a0efe %>% tidyr::drop_na(PCIS1R, PCIS2R, PCIS3R),
  title = "",
  questions = c("PCIS1R", "PCIS2R", "PCIS3R"),
  question_labels = c("Classify a social media post (task 1)", "Classify a social media post (task 2)", "What to check for fake news"),
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-wave1-age-item1}
# Classify a social media post (task 1)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PCIS1R),
  title = "Classify a social media post (task 1)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-wave1-age-item2}
# Classify a social media post (task 2)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PCIS2R),
  title = "Classify a social media post (task 2)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-wave1-age-item3}
# What to check for fake news
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PCIS3R),
  title = "What to check for fake news",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PCIS3R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-wave1-gender-item1}
# Classify a social media post (task 1)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PCIS1R),
  title = "Classify a social media post (task 1)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-wave1-gender-item2}
# Classify a social media post (task 2)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PCIS2R),
  title = "Classify a social media post (task 2)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-wave1-gender-item3}
# What to check for fake news
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PCIS3R),
  title = "What to check for fake news",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PCIS3R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-wave1-edu-item1}
# Classify a social media post (task 1)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PCIS1R),
  title = "Classify a social media post (task 1)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-wave1-edu-item2}
# Classify a social media post (task 2)
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PCIS2R),
  title = "Classify a social media post (task 2)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-wave1-edu-item3}
# What to check for fake news
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PCIS3R),
  title = "What to check for fake news",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PCIS3R"
)

result
```


:::


:::


### {{< iconify ph number-circle-two-fill >}} Wave 2


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-cis-wave2-overall}
# 
result <- create_stackedbars(
  data = data_filtered_4af682fd %>% tidyr::drop_na(PCIS1R, PCIS2R, PCIS3R),
  title = "",
  questions = c("PCIS1R", "PCIS2R", "PCIS3R"),
  question_labels = c("Classify a social media post (task 1)", "Classify a social media post (task 2)", "What to check for fake news"),
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-wave2-age-item1}
# Classify a social media post (task 1)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PCIS1R),
  title = "Classify a social media post (task 1)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-wave2-age-item2}
# Classify a social media post (task 2)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PCIS2R),
  title = "Classify a social media post (task 2)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-wave2-age-item3}
# What to check for fake news
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PCIS3R),
  title = "What to check for fake news",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PCIS3R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-wave2-gender-item1}
# Classify a social media post (task 1)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PCIS1R),
  title = "Classify a social media post (task 1)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-wave2-gender-item2}
# Classify a social media post (task 2)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PCIS2R),
  title = "Classify a social media post (task 2)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-wave2-gender-item3}
# What to check for fake news
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PCIS3R),
  title = "What to check for fake news",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PCIS3R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-wave2-edu-item1}
# Classify a social media post (task 1)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PCIS1R),
  title = "Classify a social media post (task 1)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-wave2-edu-item2}
# Classify a social media post (task 2)
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PCIS2R),
  title = "Classify a social media post (task 2)",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-wave2-edu-item3}
# What to check for fake news
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PCIS3R),
  title = "What to check for fake news",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PCIS3R"
)

result
```


:::


:::


### {{< iconify ph chart-line-fill >}} Over Time


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-overtime-overall-item1}
# Classify a social media post (task 1)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 1)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  response_filter_label = "Percentage who selected/answered correctly",
  response_filter_combine = TRUE,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  response_var = "PCIS1R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-overtime-overall-item2}
# Classify a social media post (task 2)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 2)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  response_filter_label = "Percentage who selected/answered correctly",
  response_filter_combine = TRUE,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  response_var = "PCIS2R"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-overtime-overall-item3}
# What to check for fake news
result <- create_timeline(
  data = data,
  title = "What to check for fake news",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  response_filter_label = "Percentage who selected/answered correctly",
  response_filter_combine = TRUE,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  response_var = "PCIS3R"
)

result
```


:::


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-overtime-age-item1}
# Classify a social media post (task 1)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 1)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS1R",
  group_var = "AgeGroup"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-overtime-age-item2}
# Classify a social media post (task 2)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 2)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS2R",
  group_var = "AgeGroup"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-overtime-age-item3}
# What to check for fake news
result <- create_timeline(
  data = data,
  title = "What to check for fake news",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS3R",
  group_var = "AgeGroup"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-overtime-gender-item1}
# Classify a social media post (task 1)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 1)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS1R",
  group_var = "geslacht"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-overtime-gender-item2}
# Classify a social media post (task 2)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 2)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS2R",
  group_var = "geslacht"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-overtime-gender-item3}
# What to check for fake news
result <- create_timeline(
  data = data,
  title = "What to check for fake news",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS3R",
  group_var = "geslacht"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-cis-overtime-edu-item1}
# Classify a social media post (task 1)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 1)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS1R",
  group_var = "Education"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 2


```{r perf-cis-overtime-edu-item2}
# Classify a social media post (task 2)
result <- create_timeline(
  data = data,
  title = "Classify a social media post (task 2)",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS2R",
  group_var = "Education"
)

result
```


###### {{< iconify ph chat-circle-fill >}} Question 3


```{r perf-cis-overtime-edu-item3}
# What to check for fake news
result <- create_timeline(
  data = data,
  title = "What to check for fake news",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PCIS3R",
  group_var = "Education"
)

result
```


:::


:::


:::

## {{< iconify ph palette-fill >}} Digital Content Creation


**Digital Content Creation** skills cover the ability to create and modify digital content: making presentations, combining different media, editing images/music/video, and understanding copyright rules around digital content.

```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote('Which of the <a href="#PDCCS1R" class="modal-link">following icons</a> refer to the function for cutting or removing parts of a picture (\"cropping\")?', preset = 'question')
```
[{{< iconify ph cards >}} See all Digital Content Creation results](digital_content_creation.html)


::: {.panel-tabset}

### {{< iconify ph number-circle-one-fill >}} Wave 1


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-dccs-wave1-overall}
# Identify crop icon
result <- create_stackedbars(
  data = data_filtered_984a0efe %>% tidyr::drop_na(PDCCS1R),
  title = "Identify crop icon",
  questions = "PDCCS1R",
  question_labels = "Identify crop icon",
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-wave1-age-item1}
# Identify crop icon
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PDCCS1R),
  title = "Identify crop icon",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PDCCS1R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-wave1-gender-item1}
# Identify crop icon
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PDCCS1R),
  title = "Identify crop icon",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PDCCS1R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-wave1-edu-item1}
# Identify crop icon
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PDCCS1R),
  title = "Identify crop icon",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PDCCS1R"
)

result
```


:::


:::


### {{< iconify ph number-circle-two-fill >}} Wave 2


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-dccs-wave2-overall}
# Identify crop icon
result <- create_stackedbars(
  data = data_filtered_4af682fd %>% tidyr::drop_na(PDCCS1R),
  title = "Identify crop icon",
  questions = "PDCCS1R",
  question_labels = "Identify crop icon",
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-wave2-age-item1}
# Identify crop icon
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PDCCS1R),
  title = "Identify crop icon",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PDCCS1R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-wave2-gender-item1}
# Identify crop icon
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PDCCS1R),
  title = "Identify crop icon",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PDCCS1R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-wave2-edu-item1}
# Identify crop icon
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PDCCS1R),
  title = "Identify crop icon",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PDCCS1R"
)

result
```


:::


:::


### {{< iconify ph chart-line-fill >}} Over Time


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-overtime-overall-item1}
# Identify crop icon
result <- create_timeline(
  data = data,
  title = "Identify crop icon",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  response_filter_label = "Percentage who selected/answered correctly",
  response_filter_combine = TRUE,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  response_var = "PDCCS1R"
)

result
```


:::


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-overtime-age-item1}
# Identify crop icon
result <- create_timeline(
  data = data,
  title = "Identify crop icon",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PDCCS1R",
  group_var = "AgeGroup"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-overtime-gender-item1}
# Identify crop icon
result <- create_timeline(
  data = data,
  title = "Identify crop icon",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PDCCS1R",
  group_var = "geslacht"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-dccs-overtime-edu-item1}
# Identify crop icon
result <- create_timeline(
  data = data,
  title = "Identify crop icon",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PDCCS1R",
  group_var = "Education"
)

result
```


:::


:::


:::

## {{< iconify ph chats-fill >}} Netiquette


**Netiquette** refers to proper online communication etiquette: knowing when to ask permission before sharing, choosing the right communication tool, understanding what not to share online, and using emoticons appropriately.

```{r, echo=FALSE, message=FALSE, warning=FALSE}
create_blockquote("Performance tasks: proportion correct or selected. Where items are multi-select, we show the share selecting each action.", preset = "question")
```
[{{< iconify ph cards >}} See all Netiquette results](netiquette.html)


::: {.panel-tabset}

### {{< iconify ph number-circle-one-fill >}} Wave 1


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-netiquette-wave1-overall}
# Ask for Permission to Share
result <- create_stackedbars(
  data = data_filtered_984a0efe %>% tidyr::drop_na(PNS1R),
  title = "Ask for Permission to Share",
  questions = "PNS1R",
  question_labels = "Ask for Permission to Share",
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-wave1-age-item1}
# Ask for Permission to Share
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(AgeGroup, PNS1R),
  title = "Ask for Permission to Share",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PNS1R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-wave1-gender-item1}
# Ask for Permission to Share
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(geslacht, PNS1R),
  title = "Ask for Permission to Share",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PNS1R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-wave1-edu-item1}
# Ask for Permission to Share
result <- create_stackedbar(
  data = data_filtered_984a0efe %>% tidyr::drop_na(Education, PNS1R),
  title = "Ask for Permission to Share",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PNS1R"
)

result
```


:::


:::


### {{< iconify ph number-circle-two-fill >}} Wave 2


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


```{r perf-netiquette-wave2-overall}
# Ask for Permission to Share
result <- create_stackedbars(
  data = data_filtered_4af682fd %>% tidyr::drop_na(PNS1R),
  title = "Ask for Permission to Share",
  questions = "PNS1R",
  question_labels = "Ask for Permission to Share",
  stacked_type = "percent",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  horizontal = TRUE,
  x_label = "",
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  stack_label = NULL,
  weight_var = "weging_GAMO"
)

result
```


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-wave2-age-item1}
# Ask for Permission to Share
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(AgeGroup, PNS1R),
  title = "Ask for Permission to Share",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "AgeGroup",
  stack_var = "PNS1R"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-wave2-gender-item1}
# Ask for Permission to Share
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(geslacht, PNS1R),
  title = "Ask for Permission to Share",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "geslacht",
  stack_var = "PNS1R"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-wave2-edu-item1}
# Ask for Permission to Share
result <- create_stackedbar(
  data = data_filtered_4af682fd %>% tidyr::drop_na(Education, PNS1R),
  title = "Ask for Permission to Share",
  stacked_type = "percent",
  horizontal = TRUE,
  stack_breaks = c(0, 10, 20, 30),
  stack_bin_labels = c("Incorrect", "Correct"),
  stack_map_values = list("1" = "Correct", "0" = "Incorrect"),
  stack_order = c("Incorrect", "Correct"),
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  x_var = "Education",
  stack_var = "PNS1R"
)

result
```


:::


:::


### {{< iconify ph chart-line-fill >}} Over Time


::: {.panel-tabset}

##### {{< iconify ph users-fill >}} Overall


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-overtime-overall-item1}
# Ask for Permission to Share
result <- create_timeline(
  data = data,
  title = "Ask for Permission to Share",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  response_filter_label = "Percentage who selected/answered correctly",
  response_filter_combine = TRUE,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  weight_var = "weging_GAMO",
  response_var = "PNS1R"
)

result
```


:::


##### {{< iconify mdi:human-male-male-child >}} Age


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-overtime-age-item1}
# Ask for Permission to Share
result <- create_timeline(
  data = data,
  title = "Ask for Permission to Share",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PNS1R",
  group_var = "AgeGroup"
)

result
```


:::


##### {{< iconify mdi gender-transgender >}} Gender


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-overtime-gender-item1}
# Ask for Permission to Share
result <- create_timeline(
  data = data,
  title = "Ask for Permission to Share",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PNS1R",
  group_var = "geslacht"
)

result
```


:::


##### {{< iconify ph graduation-cap-fill >}} Education


::: {.panel-tabset}

###### {{< iconify ph chat-circle-fill >}} Question 1


```{r perf-netiquette-overtime-edu-item1}
# Ask for Permission to Share
result <- create_timeline(
  data = data,
  title = "Ask for Permission to Share",
  time_var = "wave_time_label",
  chart_type = "line",
  response_filter = 1,
  y_min = 0,
  y_max = 100,
  x_label = "",
  y_label = "Percentage who selected/answered correctly",
  color_palette = c("#3D7271", "#E28D50", "#F5D76E", "#C7E6D5", "#0F6B5A", "#BABACD"),
  response_filter_label = NULL,
  weight_var = "weging_GAMO",
  response_var = "PNS1R",
  group_var = "Education"
)

result
```


:::


:::


:::



```{=html}
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    </div>
    <a href='performance_p2.html' class='pagination-btn pagination-next' aria-label='Next page'>
      <svg class='pagination-icon' width='18' height='18' viewBox='0 0 20 20' fill='none' xmlns='http://www.w3.org/2000/svg'>
        <path d='M8 16L14 10L8 4' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/>
      </svg>
    </a>
  </div>
</nav>

<!-- Pagination Navigation Script -->
<script>
(function() {
  const pageUrls = ["performance.html","performance_p2.html","performance_p3.html"];
  const pageInput = document.getElementById('page-input-bottom');
  
  if (pageInput) {
    pageInput.addEventListener('change', function() {
      const pageNum = parseInt(this.value);
      if (pageNum >= 1 && pageNum <= pageUrls.length) {
        window.location.href = pageUrls[pageNum - 1];
      } else {
        this.value = this.getAttribute('value');
      }
    });
    
    pageInput.addEventListener('keypress', function(e) {
      if (e.key === 'Enter') {
        this.blur();
      }
    });
  }
})();
</script>
```

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